RavenClaude

Make VS Code terminal tabs show 🔔 + chime the moment an agent session needs you, across many parallel Copilot/Claude terminals. Installs three layers: workspace settings (tab bell icon + audio cue), a shell prompt hook (bell on command completion), and a background /proc-io watcher that rings a terminal's PTY bell when its agent process goes idle after responding. Use in a Codespace / VS Code setup where you run multiple background agent sessions and can't tell which one is waiting for input without clicking through each tab.

Validate a Dataverse create/update payload against LIVE entity metadata in ONE pass — before you POST/PATCH — so you never fix-one-field-and-retrigger. Catches nonexistent columns, invalid option-set values, malformed/missing lookup binds, missing required fields, and the owner-not-provided (SPN-create) trap together. Run it on the FIRST create failure, or up front when a human re-fire or long run gates each test.

Claude-Code-session-state surfacing contract for the Mímir dashboard tab. Documents the five reachable on-disk sources under `~/.claude/` and `<project>/.claude/`, the encoded-path algorithm + reverse-decode fallback, the hard scrubbing/torn-write/staleness/worktree/server-parity invariants, the per-card source map, and the honest empty-state contract for in-process-only fields. Read this before authoring or modifying `_read_mimir` (in both `serve-dashboards.py` copies), the `/__mimir` endpoint, the `#/mimir` generator tab, or the Gate 49 render fixture.

Veteran-level reference for Power BI — PBIP project structure + git, semantic model design, DAX patterns + performance, deployment pipelines, refresh / gateway troubleshooting, integration with Power Platform solutions / ALM. Used by `power-bi-engineer` (primary).

Author and validate the per-entity chart-of-accounts → statement-line mapping — the bespoke, judgment-laden asset that makes the close reusable per company and where mis-statements hide. Coverage-checked with statement_engine.py --lint-map. Used by `controller`.

Roll up N entity trial balances (same period) into a group consolidation — reuses statement_engine per entity, then applies a BALANCED intercompany-elimination journal so IC receivable/payable and IC revenue/COGS net to zero, emits an entity-columns + eliminations + consolidated worksheet, and flags (does not remeasure) non-functional-currency entities for CTA. Runs scripts/consolidate.py. 'Eliminate before you consolidate.' Used by `controller`.

Translate/remeasure ONE entity's functional-currency trial balance into a presentation-currency (USD) TB before consolidate.py, via scripts/remeasure.py. Current-rate method (A&L @ closing, P&L @ average, equity @ historical; plug = CTA to OCI/equity) or temporal method (monetary @ closing, non-monetary @ historical, P&L @ average except REV_EXP_HIST @ historical; plug = remeasurement G/L to net income). Reuses statement_engine's section-signed presentation + reasoning trail — no new sign logic. Blocks on a missing/invalid rate_class, blocks when the balancing plug fails the analytical CTA self-check, and refuses a highly_inflationary + current_rate combo (ASC 830 vs IAS 29). Decision-support, not an audited remeasurement. Used by `controller`.

Turn a reconciled trial balance + a COA mapping into an income statement, balance sheet, and draft cash flow — classification-tested (blocks on unmapped accounts, catches mis-mapping that a balance-check cannot) with an honest traceability badge. Runs scripts/statement_engine.py. Used by `controller`.

Iterate an artifact until it measurably passes a BOUNDED rubric — the Convergence Engine. Objective/deterministic gates run BEFORE any model judge, the judge is a DIFFERENT model than the author (never self-grade), the stop decision is a model-free predicate, and it emits the BEST iteration (keep-best/regression-revert) under a hard iteration cap + model-call budget. The engine NEVER claims perfect — verdicts are rubric-pass | capped | plateaued | budget-exhausted with an honest residual-gaps list. Use to drive any artifact (code, prose, a visual report, an agent file) to a defensible bar; for the agent-file case it delegates to agent-quality-rubric, and for visual surfaces it composes with visual-feedback-loop.

Hardened pac solution import plus explicit baseline-aware cloud-flow reactivation for SPN-driven CI/CD — preflight, baseline, import, reactivate, verify. Managed imports leave flows Draft when the importing identity lacks connection permission; this reactivates only flows that were Active, splits transient vs durable 403s, and verifies state. Run on any managed import to TEST/PROD, or use reactivate/verify standalone.

Report-wide referential-integrity validator for a PBIR Enhanced report's `definition/` tree. Deterministic, stdlib-only `check_refs.py` that catches DANGLING cross-file references — bookmarks pointing at deleted pages/visuals, `visualInteractions` naming visuals that no longer exist, a `pageOrder` that omits/invents/duplicates a page, an `activePageName`/`activeSection` that names a missing page, and report-wide visual-name collisions. Mimics the referential-integrity half of pbir-utils `validate`/`sanitize` and pbir.tools `validate`. Complements (does NOT duplicate) ravenclaude-core's single-page `pbir-layout-engine`. Owned by `power-bi-engineer`.

Author a Vega-Lite / Deneb / SVG spec for a stated intent on any surface (web vega-embed, react-vega, Evidence, Observable, Power BI Deneb, Tableau extension/SVG, SVG-in-DAX). Six-step method: pick grammar → bind data → encode → wire interactivity → test null/empty → verify via render loop. Ships a surface-agnostic spec-patterns library. Mandatory security audit (no data.url, no remote loader, no SVG script) enforced by lint.py (Gate 101). Complements the visual-feedback-loop (render referee) and pbir-layout-engine (coordinate linter). NOT for coordinate/layout arithmetic (pbir-layout-engine) or render-loop orchestration (visual-feedback-loop).

Deterministic layout-arithmetic linter for dashboard/report page definitions. Runs seven checks — no-overlap (AABB), within-canvas, equal-gap, column-alignment, and three PBIR-specific invariants (no-empty-binding, bounded theme-override count, visualType/displayOption schema validity) — against a page JSON, a page directory, or a fixture set. Stdlib-only, no network, exit-coded for CI. The load-bearing technical core under the data-viz-designer agent; usable standalone to lint a Power BI PBIR page layout.

Scope an AI red-team engagement by threat-modeling the system (assets, attackers, trust boundaries), splitting safety from security, traversing the attack-taxonomy decision tree to a prioritized OWASP LLM Top 10 / MITRE ATLAS attack list, and setting the rules of engagement plus likelihood×impact success and severity criteria. Reach for this when the user asks "how should we red-team this LLM feature?", "what should we attack first?", "is this a safety or a security problem?", or "what are the rules of engagement?". Used by `ai-redteam-lead` (primary).

Triage red-team findings by likelihood×impact and drive defense-in-depth remediation — layered input/output guardrails, injection-resistant prompt structure, least-privilege tool scoping, allow-lists, human-in-the-loop on high-impact actions, output-handling hygiene, and rate/cost limits — then retest each fix with the exact attack that found it and bake it into the regression harness. Reach for this when the user asks "we have a pile of red-team findings — what do we fix and how?", "harden our LLM against prompt injection", or "how do we stop the agent from being tricked into tool calls?". Used by `adversarial-testing-engineer` (primary).

Execute the prioritized attacks against an AI system within the rules of engagement — direct and indirect prompt injection, jailbreaks (roleplay, encoding, many-shot, crescendo), training-data extraction and data exfiltration, agentic tool-abuse / excessive agency, and multimodal attacks — capturing each as a reproducible payload plus transcript, then automating what repeats into a PyRIT / Garak / Promptfoo red-team / Giskard harness with a scorer and a CI regression gate. Reach for this when the user asks "run the jailbreak and injection attacks", "build an automated red-team suite", or "can our agent be tricked into calling tools it shouldn't?". Used by `adversarial-testing-engineer` (primary).

Pick the right audio-DSP architecture for a described product by traversing the audio-DSP architecture decision tree (latency tolerance → processing model → time-vs-frequency domain → fixed-vs-float + denormals → platform/plugin format + audio I/O), then return the recommended processing model, latency & buffer-size budget, sample rate/bit depth, numeric strategy, algorithm approach (IIR/FIR/FFT-STFT, oversampling), framework + plugin format + audio backend, and the conditions that would flip the choice. Reach for this when the user asks "block or sample-by-sample?", "what latency/buffer size?", "JUCE + VST3/AU/CLAP or Web Audio?", "fixed-point or float?", or "IIR biquad vs FIR vs FFT for this effect?". Used by `audio-dsp-architect` (primary).

From an effect or product goal and its architecture, derive the signal-processing chain — the block diagram (stage order), the per-stage algorithm (IIR biquad / FIR / FFT-STFT / delay / dynamics), the per-stage and total latency, the sample rate and gain-staging/headroom, the oversampling plan for any nonlinearity, and the parameter list with smoothing needs — captured in the DSP design spec. Reach for this when the user asks "design the effect chain for this", "what order should these processors go in?", or "map out the DSP stages and their latency". Used by `dsp-implementation-engineer` and `audio-dsp-architect`.

Implement a DSP stage as real-time-safe code in the audio callback (no locks, no allocation, no syscalls, no unbounded work — everything pre-allocated at prepare time), handle denormals with flush-to-zero, pass parameters lock-free (atomic / SPSC FIFO) with per-sample smoothing, then optimize the profiled hot loop with SIMD (SSE/AVX/NEON/CMSIS-DSP) and verify with objective measurement (null test, THD+N, impulse/frequency response, RT-safety audit). Reach for this when the user asks "write the real-time-safe processBlock", "optimize this hot loop", "pass params without a data race", or "null-test / measure this effect". Used by `dsp-implementation-engineer` (primary).

Pick the right genomics workflow engine, reference build, tool chain, compute strategy, and reproducibility approach for a described analysis by traversing the bioinformatics pipeline decision tree (assay/question → curated community pipeline exists? → portability/team fluency → HPC vs cloud → reference build), then return the recommended engine (Nextflow/nf-core / Snakemake / WDL+Cromwell / CWL), the reference (GRCh38 vs T2T-CHM13) with the build hazards, the aligner/variant-caller chain, the compute plan (Slurm vs cloud Batch/spot + cost shape), the reproducibility approach, the validation truth set, and the conditions that would flip the choice. Reach for this when the user asks "Nextflow vs Snakemake vs WDL?", "GRCh38 or T2T-CHM13?", "HPC or cloud for this pipeline?", or "how do we make this reproducible?". Used by `bioinformatics-workflow-architect` (primary).

From a scientific question, an assay, and a sample design, derive the concrete genomics analysis workflow — the per-sample step graph (QC, trimming, alignment, dedup, BQSR-or-not, variant calling or quantification), the cohort/joint step (joint genotyping or differential-expression model), the reference build and its matching accessory files, and the validation truth set — captured as an analysis plan. Reach for this when the user asks "design the WGS germline workflow", "what steps does this RNA-seq analysis need?", or "how do we structure the per-sample and cohort steps?". Used by `genomics-pipeline-engineer` and `bioinformatics-workflow-architect`.

Implement a designed genomics workflow in the chosen engine, containerize and pin every tool for reproducibility, scale it with scatter/gather on HPC Slurm or cloud Batch (spot on the fault-tolerant steps), and validate it against a GIAB/GA4GH hap.py truth set — then produce a pipeline-validation report. Reach for this when the user asks "build this pipeline in Nextflow/Snakemake/WDL", "containerize and pin it for reproducibility", "scale/cost-optimize this on Slurm or cloud", or "benchmark our variant calls against GIAB". Used by `genomics-pipeline-engineer` (primary).

Scope and architect a digital twin for a described asset and decision by traversing the digital-twin architecture decision tree (decision it informs → twin type → shadow-vs-bidirectional → descriptive/predictive/prescriptive → modeling approach → fidelity → sync → platform), then return the twin scope/type, the fidelity level (only as much as the decision needs), the modeling approach (physics vs data-driven/surrogate/reduced-order vs hybrid), the platform (DTDL+Azure Digital Twins / AAS / Eclipse Ditto / Omniverse / Unity-Unreal / Bentley-Siemens), the state-sync strategy, and the conditions that would flip the choice. Reach for this when the user asks "we want a twin of <X> — where do we start?", "physics vs data-driven vs hybrid?", "how much fidelity?", "which twin platform?", or "shadow or bidirectional?". Used by `digital-twin-architect` (primary).

From a physical asset and its telemetry, derive the twin's data and synchronization model — the signal list and sampling rates, the transport protocol (MQTT / OPC-UA / Kafka), the edge-vs-cloud split, the sync-latency budget the decision tolerates, the binding of each signal to the twin's model properties (DTDL properties / AAS submodel elements), and the drift-detection + recalibration policy. Reach for this when the user asks "what data does this twin need and how often?", "map our sensor stream to the twin model", or "how fresh must the twin be?". Used by `twin-integration-engineer` and `digital-twin-architect`.

Build a digital twin end to end — ingest the telemetry (MQTT / OPC-UA / Kafka, edge-vs-cloud) and bind it to the model (DTDL properties / AAS submodels), wire the physics or reduced-order/surrogate model, run simulation and what-if scenarios against live twin state, stand up 3D or dashboard visualization where it earns its keep, and — the step teams skip — validate fidelity against the real asset (predicted-vs-actual SLIs, error bounds, calibration) plus a drift monitor. Reach for this when the user asks "ingest our stream into the twin", "run a what-if simulation", "does the twin match the asset?", or "the twin has drifted". Used by `twin-integration-engineer` (primary).

Audit a funeral home's pricing and disclosures against the FTC Funeral Rule and clear the cremation-authorization and vital-records gates. Check the General Price List, Casket Price List, and Outer Burial Container list for required disclosures and itemization (no forced packages, embalming-not-required, telephone-price disclosure, no misrepresentation); confirm the cremation authorizing agent, positive ID, written authorization, and unbroken chain-of-custody before any irreversible step; and sequence the death certificate, certification, and disposition permits. Reach for this when the user asks "is our GPL compliant?", "what authorizes a cremation?", or "what vital records/permits do we need?". Used by `funeral-arrangement-and-compliance-specialist` (primary). Not legal advice — verify the current Rule + state law with counsel.

Read and manage the funeral-home case-flow pipeline (first call → removal/transfer → arrangement → preparation → services → billing → aftercare), find the constraint stage that gates throughput, size staffing and on-call/removal capacity against call volume, decide build-vs-contract for removals or preparation, and produce the fulfillment plan for the arranged services — all while protecting the family experience, not just the margin. Reach for this when the user asks "why are families waiting?", "are we staffed right for our volume?", "where's the bottleneck in our case flow?", or "can we fulfill these services on our capacity?". Used by `funeral-operations-lead` (primary). Not legal/financial advice — verify licensing and benchmarks.

Run a grief-aware, FTC-Funeral-Rule-compliant arrangement — from first-call intake through the arrangement conference to documented, itemized selections and disposition. Traverse the deathcare-compliance decision tree so the right disclosures (GPL, CPL, Outer Burial Container list, telephone-price, embalming-not-required, no-misrepresentation) are given at the right moment, itemize without forcing a package, select the disposition and its requirements, and capture it in the arrangement worksheet. Reach for this when the user asks "walk me through the arrangement conference", "how do we handle a first call", or "arrange this service correctly". Used by `funeral-arrangement-and-compliance-specialist` (primary). Not legal advice — verify jurisdiction-specific items with counsel.

Build a prioritized, capacity-weighted grant pipeline and decide whether to pursue each opportunity by scoring funder fit BEFORE effort and running a real go/no-go — traverse the grants-lifecycle decision tree (grant type → funder fit → go/no-go), then return a fit score per prospect, a pursue/pass verdict weighing eligibility, capacity to deliver, cost-to-apply vs expected value, and compliance burden, plus the conditions that would flip each call. Reach for this when the user asks "should we apply for this grant?", "is this funder a fit?", "build our grant pipeline", or "go/no-go on this RFP". Used by `grants-strategy-lead` (primary).

Keep an awarded federal grant clean from setup through closeout — run the 2 CFR 200 cost-principle test (allowable/allocable/reasonable) on every charge, establish and apply the indirect rate (NICRA vs de minimis on MTDC), keep time & effort certified, produce the SF-425 FFR and the RPPR/SF-PPR on the award calendar, make the 2 CFR 200.331 subrecipient-vs-contractor determination and stand up subrecipient monitoring, and carry the award to an audit-ready closeout (Single Audit threshold, documentation trail, final reports). Reach for this when the user asks "can we charge this cost?", "what indirect rate?", "build the FFR/RPPR", "subrecipient or contractor?", or "are we audit-ready?". Used by `grants-compliance-and-reporting-specialist` (primary). Not legal/accounting advice.

Turn a program into a fundable, NOFO-rubric-aligned proposal by chaining the program-design spine — needs statement → logic model / theory of change → SMART objectives → evaluation plan → budget & budget narrative (every line tied to an activity) — then assemble the LOI or full-proposal package (narrative, attachments, SF-424 family if federal, a reusable boilerplate library, and a NOFO-criteria crosswalk so nothing scored is unaddressed). Reach for this when the user asks "draft the logic model / needs statement", "write the narrative for this NOFO", or "assemble the full proposal / LOI". Used by `grants-strategy-lead` (primary).

Design the museum's earned-and-contributed revenue engine — the admissions/pricing model (timed ticketing / dynamic / pay-what-you-wish / free-admission, with the access-vs-revenue trade-off modeled), a tiered membership program built for behavior with an acquisition-and-renewal/retention engine (first-year renewal focused), the development levers (patron cultivation, corporate sponsorship, galas), and a named earned-vs-contributed revenue-mix target with its levers. Reach for this when the user asks "timed/dynamic/free admission?", "design our membership tiers and renewal program", "grow venue-rental/retail earned revenue", or "what's a healthy earned-vs-contributed mix?". Used by `museum-operations-lead` (primary).

Steward a collection and build the exhibitions it feeds — run the accession/deaccession ethics gate (AAM/AAMD; deaccession proceeds fund collection care/acquisition only), cataloging, provenance & NAGPRA due diligence, condition reporting, loans in/out, insurance/valuation, storage & environmental controls, the collections-CMS fit (TMS/Axiell/PastPerfect/CollectionSpace), and the exhibition project lifecycle (concept → checklist & loans → interpretation → budget → schedule → install/condition → evaluation). Reach for this when the user asks "should we accession/deaccession this?", "run the provenance/NAGPRA due diligence", "TMS vs Axiell vs PastPerfect vs CollectionSpace?", or "plan this temporary/traveling exhibition". Used by `collections-and-engagement-specialist` (primary).

Publish a collection online, rights- and cultural-sensitivity-cleared — run the copyright/rights gate and the source-community consultation, decide open-access vs restricted (CC0/CC-BY vs a rights-restricted license, with rights statements), and stand up the IIIF image/presentation stack + DAMS + online catalog + virtual exhibitions, defaulting toward openness where rights allow while cultural sensitivity gates the publish button. Reach for this when the user asks "publish our collection online", "open access or not?", "set up IIIF for our images", or "can we put this culturally sensitive material online?". Used by `collections-and-engagement-specialist` (primary).

Run a self-storage facility's operations — choose the operating model (staffed vs hybrid vs remote-unmanned + kiosk) against the site's volume and labor market, set the staffing, harden security and access control (gate/keypad, individual door alarms, cameras with retention, lighting, overlock discipline), keep climate control, maintenance, and curb appeal to standard, protect the move-in/move-out flow (agreement, protection-plan and autopay offer, gate-code provisioning, overlock-release), and standardize a multi-site portfolio with an SOP, PMS-config parity, a district-manager cadence, and a per-site scorecard. Reach for this when the user asks "staffed or remote/kiosk?", "how do I tighten security after a break-in?", "our move-in process is leaky", or "how do I run five sites to one standard?". Used by `self-storage-operations-lead` (primary).

Turn a self-storage facility's occupancy and rate picture into a revenue plan by reading physical vs economic occupancy, comparing street rate to in-place rate by unit type, designing the ECRI program (existing-customer rate increases — cadence, increase size by tenure and gap, churn guardrail), setting dynamic-pricing floors and ceilings, rebalancing the unit mix, and pricing promotions against the ECRI that recovers them — returning the recommended moves with projected NOI lift and the conditions that would flip them. Reach for this when the user asks "how do I raise rates on existing tenants?", "my street and in-place rates are far apart", "should I run a $1-first-month promo?", or "why is my full facility not making money?". Used by `storage-revenue-and-occupancy-specialist` (primary).

Run a self-storage tenant from first missed payment to lien auction as a disciplined, state-specific process — start with prevention (autopay enrolment, late-fee discipline), then walk the statutory lien timeline (late fees → gate lockout/overlock → pre-lien and lien notices → advertising/public notice → auction via StorageTreasures/Lockerfox → sale of goods → surplus handling), flagging at every step that lien law varies by US state, attaching the state and a retrieval date, and marking the whole thing operational guidance and NOT legal advice. Reach for this when the user asks "I have tenants 60+ days past due — what now?", "walk me through the lien and auction process", "when can I overlock a unit?", or "how do I handle auction surplus?". Used by `storage-revenue-and-occupancy-specialist` (primary).

Diagnose what to fix first for a described site by walking the SEO strategy decision tree (crawl → render → index → understand → rank), fixing the lowest broken rung first, then return the priority diagnosis, the indexation strategy, the E-E-A-T posture, and the conditions that would reorder the priorities. Reach for this when the user asks "our organic traffic is flat — where do we start?", "what should we fix first for SEO?", "which pages should we index or noindex?", or "how do we rank in a helpful-content world?". Used by `seo-strategy-architect` (primary).

From a site's target queries and page classes, design the crawl-efficient information architecture (flat depth, hub-and-spoke topic clusters), the internal-linking model that flows authority to the money pages, and the topical-authority / entity map that tells search engines what the site should own. Reach for this when the user asks "how should we structure the site and internal links?", "design our topic clusters / content model", or "what's our topical-authority / entity map?". Used by `seo-strategy-architect` (primary) and `seo-implementation-engineer`.

Implement and verify the technical-SEO layer for a site — crawlability (robots.txt, XML sitemaps, log-file analysis), rendering (CSR→SSR/SSG/prerender), indexation controls (canonical, meta-robots noindex, hreflang), JSON-LD schema.org structured data for rich-result eligibility, Core Web Vitals (INP/LCP/CLS on field data), and redirect-mapped site migrations — each checked against GSC / URL Inspection / the Rich Results Test / server logs. Reach for this when the user asks "fix our crawl/index", "our SPA doesn't rank — is it rendering?", "add schema markup", "improve Core Web Vitals", or "run our site migration without losing rankings". Used by `seo-implementation-engineer` (primary).